Every revolution starts with a question. For the last two years, a single question has been bouncing around my head:
What if we could build an AI system which acts like a team member, fully autonomous, adaptable and self learning, so it can perform commercial work for us (as we sit back and hold our breath)?
That's how our idea for HAL was born, as a real moment of curiosity, skepticism, and (I’ll admit) a bit of mischief. I have always loved experimenting, especially those that break the status quo. This particular experiment was not just to chase efficiency, it's a genuine question about how far AI can become a team member. I wanted to see how far we could push the boundaries of AI—and, yes, have some fun along the way.
01 | Moving Beyond the Norm
02 | Why HAL? A Name With a Wink
03 | The Architect’s Table: Sketches, Data Flows, and Self-Learning Dreams
04 | The Real Premise: Scaling Our Proven Practice
05 | Wrestling with Big Questions
06 | Feeding Our Team's Inner Inventor
What is Next? | Join the Journey: Exploring, Building, and Shaping HAL Together